






"""simple docstring"""



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A						:			List[str]    							=						logging.get_logger(__name__)

__A						:			List[str]    							=						'''▁'''

__A						:			Union[str, Any]    							=						{'''vocab_file''': '''sentencepiece.bpe.model'''}

__A						:			List[Any]    							=						{
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

__A						:			str    							=						{
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}





class    _UpperCAmelCase			(					__snake_case				):
					SCREAMING_SNAKE_CASE_						:							Dict				       =  VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE_						:							List[Any]				       =  PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE_						:							int				       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE_						:							Dict				       =  ["""input_ids""", """attention_mask"""]




					def __init__(							self	:		Dict       ,		A	:		Any       ,		A	:		List[str]="<s>"       ,		A	:		int="</s>"       ,		A	:		Any="</s>"       ,		A	:		Optional[int]="<s>"       ,		A	:		Union[str, Any]="<unk>"       ,		A	:		Union[str, Any]="<pad>"       ,		A	:		int="<mask>"       ,		A	:		Optional[Dict[str, Any]] = None       ,		**A	:		Optional[Any]       ,		)    ->    None:
							# Mask token behave like a normal word, i.e. include the space before it
							lowercase_					:					Any							=  AddedToken(__SCREAMING_SNAKE_CASE       ,		lstrip=__SCREAMING_SNAKE_CASE       ,		rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE  ) else mask_token

							lowercase_					:					Union[str, Any]							=  {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=__SCREAMING_SNAKE_CASE       ,		eos_token=__SCREAMING_SNAKE_CASE       ,		unk_token=__SCREAMING_SNAKE_CASE       ,		sep_token=__SCREAMING_SNAKE_CASE       ,		cls_token=__SCREAMING_SNAKE_CASE       ,		pad_token=__SCREAMING_SNAKE_CASE       ,		mask_token=__SCREAMING_SNAKE_CASE       ,		sp_model_kwargs=self.sp_model_kwargs       ,		**__SCREAMING_SNAKE_CASE       ,		)

							lowercase_					:					Any							=  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(str(__SCREAMING_SNAKE_CASE  )  )
							lowercase_					:					Optional[Any]							=  vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							lowercase_					:					Any							=  {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							lowercase_					:					List[Any]							=  1

							lowercase_					:					Tuple							=  len(self.sp_model  ) + self.fairseq_offset
							lowercase_					:					str							=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}




					def __getstate__(							self	:		List[Any]  )    ->    int:
							lowercase_					:					Any							=  self.__dict__.copy()
							lowercase_					:					Dict							=  None
							lowercase_					:					List[str]							=  self.sp_model.serialized_model_proto()
							return state




					def __setstate__(							self	:		Tuple       ,		A	:		Any  )    ->    List[Any]:
							lowercase_					:					Optional[Any]							=  d

							# for backward compatibility
							if not hasattr(self       ,		'''sp_model_kwargs'''  ):
									lowercase_					:					str							=  {}

							lowercase_					:					List[str]							=  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )




					def     A    (							self	:		List[str]       ,		A	:		List[int]       ,		A	:		Optional[List[int]] = None  )    ->    List[int]:

							if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowercase_					:					str							=  [self.cls_token_id]
							lowercase_					:					Union[str, Any]							=  [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep




					def     A    (							self	:		Union[str, Any]       ,		A	:		List[int]       ,		A	:		Optional[List[int]] = None       ,		A	:		bool = False  )    ->    List[int]:

							if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=__SCREAMING_SNAKE_CASE       ,		token_ids_a=__SCREAMING_SNAKE_CASE       ,		already_has_special_tokens=__SCREAMING_SNAKE_CASE  )

							if token_ids_a is None:
									return [1] + ([0] * len(__SCREAMING_SNAKE_CASE  )) + [1]
							return [1] + ([0] * len(__SCREAMING_SNAKE_CASE  )) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE  )) + [1]




					def     A    (							self	:		List[str]       ,		A	:		List[int]       ,		A	:		Optional[List[int]] = None  )    ->    List[int]:
							lowercase_					:					Optional[int]							=  [self.sep_token_id]
							lowercase_					:					Optional[int]							=  [self.cls_token_id]

							if token_ids_a is None:
									return len(cls + token_ids_a + sep  ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]




					@property
					def     A    (							self	:		Any  )    ->    Tuple:
							return len(self.sp_model  ) + self.fairseq_offset + 1  # Add the <mask> token




					def     A    (							self	:		Optional[int]  )    ->    str:
							lowercase_					:					int							=  {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE  ): i for i in range(self.vocab_size  )}
							vocab.update(self.added_tokens_encoder  )
							return vocab




					def     A    (							self	:		Union[str, Any]       ,		A	:		str  )    ->    List[str]:
							return self.sp_model.encode(__SCREAMING_SNAKE_CASE       ,		out_type=__SCREAMING_SNAKE_CASE  )




					def     A    (							self	:		Optional[int]       ,		A	:		Tuple  )    ->    Tuple:
							if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
							lowercase_					:					List[Any]							=  self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE  )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




					def     A    (							self	:		Union[str, Any]       ,		A	:		List[Any]  )    ->    str:
							if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset  )




					def     A    (							self	:		str       ,		A	:		Tuple  )    ->    Any:
							lowercase_					:					Union[str, Any]							=  ''''''.join(__SCREAMING_SNAKE_CASE  ).replace(__SCREAMING_SNAKE_CASE       ,		''' '''  ).strip()
							return out_string






					def     A    (							self	:		Union[str, Any]       ,		A	:		str       ,		A	:		Optional[str] = None  )    ->    Tuple[str]:
							if not os.path.isdir(__SCREAMING_SNAKE_CASE  ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
									return
							lowercase_					:					List[Any]							=  os.path.join(
							    __SCREAMING_SNAKE_CASE       ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

							if os.path.abspath(self.vocab_file  ) != os.path.abspath(__SCREAMING_SNAKE_CASE  ) and os.path.isfile(self.vocab_file  ):
									copyfile(self.vocab_file       ,		__SCREAMING_SNAKE_CASE  )
							elif not os.path.isfile(self.vocab_file  ):
									with open(__SCREAMING_SNAKE_CASE       ,		'''wb'''  ) as fi:
											lowercase_					:					int							=  self.sp_model.serialized_model_proto()
											fi.write(__SCREAMING_SNAKE_CASE  )

							return (out_vocab_file,)











"""simple docstring"""





import functools
from typing import Any







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						list[str]					) ->							bool:

  # Validation
  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or len(_lowerCamelCase					) == 0:
    raise ValueError('the string should be not empty string'					)

  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or not all(
      isinstance(_lowerCamelCase						,							_lowerCamelCase					) and len(_lowerCamelCase					) > 0 for item in words					):
    raise ValueError('the words should be a list of non-empty strings'					)

  # Build trie
  lowerCamelCase_       	=		{}
  lowerCamelCase_       	=		'WORD_KEEPER'

  for word in words:
    lowerCamelCase_       	=		trie
    for c in word:
      if c not in trie_node:
        lowerCamelCase_       	=		{}

      lowerCamelCase_       	=		trie_node[c]

    lowerCamelCase_       	=		True

  lowerCamelCase_       	=		len(_lowerCamelCase					)

  # Dynamic programming method
  @functools.cache
  def is_breakable(_lowerCamelCase			:						int					) -> bool:
    if index == len_string:
      return True

    lowerCamelCase_       	=		trie
    for i in range(_lowerCamelCase						,							_lowerCamelCase					):
      lowerCamelCase_       	=		trie_node.get(string[i]						,							_lowerCamelCase					)

      if trie_node is None:
        return False

      if trie_node.get(_lowerCamelCase						,							_lowerCamelCase					) and is_breakable(i + 1					):
        return True

    return False

  return is_breakable(0					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()




